How to go from migration to modernization by building a strong cloud and automation strategy

The pandemic forced many organizations to adapt to remote and hybrid work, accelerating their plans to move to the cloud. Now Canadian business leaders are focusing their efforts on improving enterprise agility, automation and modernization, according to KPMG’s latest Global Tech Report.


57% of Canadian respondents say improving enterprise agility and modernization is a top driver if digital transformation


78% of Canadian respondents believe their organization is either extremely or very effective at using technology to advance their business strategy


93% of Canadian respondents said they're advanced in their adoption of cloud technology


59% of Canadian respondents say that 40% or more of their enterprise workloads are now in the cloud

The survey found that 93 per cent of Canadian business leaders—versus 89 per cent globally—say they’re “advanced” in cloud adoption. Yet, surprisingly, when asked to rank the benefits of cloud and automation work they’ve seen over the previous year, “drives innovation,” “reduces technology debt” and “accelerates adoption of advanced technology” were ranked the lowest.

This counters what many consider to be a benefit when they start on their cloud and automation journeys. That’s because laying the foundation for cloud and automation doesn’t always bring immediate benefits. However, there’s often an expectation that cloud is a silver bullet for many business problems. As a result, stakeholders may be disappointed when they don’t see any immediate improvement to those problems.

Additional complexity is also born out of Hybrid / Multi Cloud requirements – this is a reality for many organizations. Few Canadian enterprises are starting their journeys with a green field and need to establish a Hybrid cloud environment combining private and public clouds with on-premise infrastructure. Most organizations will have a hybrid cloud environment (which includes private and public cloud as well as on-premise infrastructure) and a multi-cloud environment (with multiple cloud hyperscalers rather than a single cloud provider). In some cases, this is necessary: In the Canadian banking sector, for example, the Office of the Superintendent of Financial Institutions (OSFI) has warned against the operational risk of relying on a single hyperscaler.

Determining the right cloud solution is based on your organization’s unique needs and typically involves more than one cloud provider and type of deployment.

Best practices for future-proofing your cloud strategy

Building a strong foundation for the future means taking a cloud smart approach—versus a cloud first approach—and involves looking at how you can apply technology to solve both technical and business challenges. It means starting with the business problem and working backwards, versus starting with the technology.

These best practices can help you achieve more value from your cloud assets:

1. Consider cloud smart vs. cloud first

Some business stakeholders are overly focused on data center exit at all costs, focusing on moving first and modernizing later. This can be a tough sell to business units who may not understand what’s in it for them to move. Cloud first approaches often fail to consider the increased cost of moving technical debt to the cloud. Taking a cloud smart approach versus simply lifting and shifting to cloud means connecting your cloud enablement roadmap to business priorities.

2. Understand the true costs of cloud

As organizations move more workloads to the cloud, cost optimization becomes increasingly important. Early wins may not have built in the automation required to track cloud spend, holistically and at a granular level. The ability to map cloud spend back to a specific application, effectively telling an application level ROI story, can help build confidence with the finance team, which is typically left to deal with the challenge of cloud being an OpEx versus CapEx expense.

3. Increase visibility through integration

Do you know how many cloud assets are being used across the enterprise? Do you know how many are siloed or duplicated? What about shadow IT? Some departments might be running software-as-a-service (SaaS) applications without IT support (or knowledge), but it’s important to understand how these applications are integrated into the organization’s overall IT systems and whether they’re being used optimally.

4. Include risk stakeholders in the process

There can be significant costs related to developing a secure architecture that will meet the needs of your risk control partners. That’s why integrating those stakeholders into the architecture and design process is so important. It’s worth noting that many emerging use cases, for example, AI use cases often make increasing use of platform-as-a-service (PaaS) capabilities in the cloud yet PaaS tends to offer fewer controls over the underlying services than infrastructure-as-a-service (IaaS). Be sure to understand which controls are being made available by the cloud provider.

5. Adopt a bias towards automation

It is crucial for cloud programs to utilize automation pipelines to manage the cloud services lifecycle, from creation to tear-down, instead of relying on manual processes. Though they come at a price, the consistency, repeatability, and accelerated approval times are well worth the investment. With automation, teams avoid human error and ensure that each service deployment aligns with the enterprise’s opinion on acceptable risk. Ultimately, “opinionated” cloud automation pipelines help organizations to maintain control over their cloud environments while reducing operational cost and minimizing risk.

6. Manage your cloud providers

When you try to move a workload from one platform to another, there could be hidden fees or egress charges. Fully understanding the cost structure and being able to properly negotiate with cloud providers on egress charges are key to avoiding unanticipated costs. This may require using third-party platforms with solutions that can be reused across multiple cloud platforms. You may want to consider hiring a chief cloud officer or working with a cloud advisor to understand the optimal approach to managing workloads.

7. Upskill your team

Have a plan to democratize the skills required to be effective with cloud architectures. If the majority of your cloud engineering knowledge is concentrated in your Centre of Excellence, eventually your CoE will fail—resources will burn out because they’re spending an increased amount of time supporting teams that are using cloud versus enabling new cloud capabilities. A best practice is to hire for dedicated roles that bridge knowledge gaps by understanding both the business domain and the technology that needs to be built by the cloud engineering team.

8. Validate your processes

Have you adopted cloud in a way that allows you to continue to advance your cloud and automation strategies? This technology continues to evolve at pace, which can lead to false starts or cause teams to miss key components. In some cases, organizations have had to hit pause while they validate the risk control frameworks used to migrate early applications. It’s a good idea to move forward having validated that your processes and existing automation platforms meet any regulatory compliance requirements.

9. Prioritize hiring the right talent

Lack of skills and talent, as well as suboptimal data, are the biggest challenges to cloud and automation goals, according to KPMG’s Global Tech Report. Overcoming these challenges means growing talent internally and modernizing talent retention processes, increasing the ability to measure cloud spend at an application level, and reducing time spent supporting business teams with existing services versus time spent enabling new capabilities.

The majority of organizations have moved at least some of their strategic workloads to the cloud. But optimizing and modernizing those workflows requires establishing clear measures and key performance indicators on which investments can be prioritized. It’s also important to have feedback loops supported by automation to validate that your KPIs are, in fact, working.

Successful organizations attack ‘low-hanging fruit’ early and in parallel to foundational work. They make careful decisions on what to rehost versus refactor or rearchitect, keeping in mind that legacy workloads in the cloud can be difficult to manage and automate. Perhaps most importantly, they’re investing in their people and aligning technology with their business objectives, so the move to cloud is truly about modernization—not just migration.

A few words on generative AI

Generative AI has the potential to significantly impact cloud migration and help optimize cloud automation by enabling organizations to automate various tasks and processes, reducing the need for manual intervention, and ultimately improving efficiency and productivity.

Generative AI models can be trained to understand patterns in data and generate new data that resembles the original dataset. In the context of cloud migration, generative AI can help organizations automate the process of transferring data from on-premises systems to the cloud, which can be a time-consuming and complex task. Generative AI can be used to analyze data sets, generate migration scripts, and even automate the migration process itself, reducing the amount of time and resources required to complete the migration.

Generative AI can also be used to optimize cloud automation by enabling organizations to automate the configuration and management of cloud infrastructure. For example, generative AI models can be used to analyze data from monitoring tools and automatically adjust cloud resources, such as scaling up or down based on demand. This can help organizations optimize their cloud usage and reduce costs by only using the resources they need, when they need them.

Overall, generative AI has the potential to revolutionize cloud migration and automation by enabling organizations to automate complex processes, reduce costs, and improve efficiency. As the technology continues to advance, we can expect to see more applications of generative AI in the cloud computing space, like Auto-GPT.

How we can help

KPMG in Canada can assist you in streamlining processes to achieve operational excellence. No matter where you are in your journey, from reviewing your current processes to implementing game-changing new technology, our team of experienced professionals is here to help.

With a network of over 14,000 data and technology professionals in various KPMG member firms – including leading data scientists and engineers – and alliances with some of the top global technology providers, our Canadian cross-functional team (including KPMG Lighthouse, our centre of excellence for data, AI and exponential technologies) leverages the latest data, analytics, intelligent automation and artificial intelligence technologies to help build and deliver solutions for your unique business needs. We’re able to offer a collaborative and objective approach to select the best vendors for your business, and can help to develop, communicate, and execute the right cloud strategy using our proven multidisciplinary approach.

Dina O’Donnell, Enterprise Architect Leader, Partner, KPMG in Canada
JQ Lien, Data + AI Leader, Partner, KPMG in Canada
Francois Gaudreau, Partner, Management Consulting, Intelligent Automation
Tahanie Thabet, Partner, Management Consulting, KPMG in Canada

Connect with us

Stay up to date with what matters to you

Gain access to personalized content based on your interests by signing up today

Connect with us